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Data Lake Delivery Lead

Data Lake Delivery Lead

CPUS Engineering Staffing Solutions Inc.Toronto
30+ days ago
Job description

Job Overview

  • Lead and manage multiple projects simultaneously, ensuring timely completion, quality and budget adherence.
  • Guide, create, and coach the Data Engineering Scrum Team on Agile practices and principles to deliver high quality data products.
  • Facilitate all Scrum events, including sprint planning, daily stand-ups, backlog grooming, sprint review and retrospectives.
  • Identify, assess, and mitigate risks, ensuring proactive management and resolution of issues.
  • Ensure projects meet our client’s quality standards, compliance requirements, and regulatory expectations.
  • Manage project budgets, forecasts, and financial reports, ensuring accurate tracking and reporting.
  • Develop and implement effective communication plans, ensuring stakeholders are informed and engaged throughout the project lifecycle.
  • Identify, analyze, and resolve project and support issues, ensuring minimal impact on project timelines and budgets.
  • Continuously improve project delivery processes, identifying opportunities for efficiency gains and best practices adoption.
  • Work closely with the Senior Manager, Data Lake Platform Engineering to set priorities and assign sprint tasks to the appropriate team members.
  • Facilitate project and scrum meetings by bringing the relevant resources together on critical issues and delivery discussions.

Qualifications

EDUCATION

Completion of a four-year University education in computer science, computer / software engineering or another relevant program.

EXPERIENCE

  • Experience with managing multiple projects and priorities.
  • Strong communication, facilitation and negotiation skills.
  • Experience with managing agile sprints for development teams.
  • Knowledge of change management and risk management related to data lake / data warehouse projects.
  • In-depth knowledge of Agile frameworks or methods such as Scrum and Kanban.
  • Solid understanding of traditional project management principles and practices.
  • A good understanding of data pipeline design pattern to ingest data into a data warehouse.
  • A good understanding of various database and storage technologies and their use cases.
  • A good understanding of data governance and data quality initiatives.
  • A good understanding of data integration and data warehousing technologies.
  • Excellent communication and collaboration skills.